基于分层模糊控制的智能车辆的横向控制模型

车辆的横向控制是智能交通中智能车辆自主导航技术的关键技术。在总结以往研究成果的基础上,提出了动态目标位置概念,以便更加真实地描述车道变换的特性;为了减少模糊控制器的规则数量,实现实时控制的目的,采用了分层模糊控制的控制器设计方案;在车辆的横向控制中,以三次样条曲线作为车道变换的路径拟合曲线,较为灵活地表现了车道变换的特性。仿真结果表明,被控车辆能够沿着虚拟的路线平滑地变换车道,较为理想地模拟实际交通环境中车辆横向运动的特性。     

基于动态目标位置的智能车辆控制研究

为了真实地模拟驾驶员在动态环境中变换车道、避让静态和动态障碍物的行为方式,提出了动 态目标位置概念,并采用三次样条曲线作为车道变换的路径拟合曲线．本文以模糊逻辑为控制策略,以T-S 模 糊模型为控制结构,以自适应神经网络为隶属度函数的参数调整手段,设计出一种智能车辆横向运动控制器, 并通过计算机仿真实现．结果表明,基于动态目标位置概念的控制器设计具有较好的控制性能,能够较为理 想地模拟实际交通环境中车辆横向运动的特性．     

智能车辆视觉导航横向模糊控制策略仿真研究

针对智能车辆视觉导航横向控制中存在的适应性问题,提出了一种基于横摆角速度与横向偏差的智能车辆横向模糊控制策略。根据车辆于车道内的位置状态信息,建立了横摆角速度预测模型、7自由度车辆操纵动力学模型以及横向模糊推理模型。仿真结果表明,与常规位置偏差控制策略相比较,采用横向模糊控制策略不仅能提高智能车辆横向控制的精确性,并且具有很好的鲁棒性和自适应性。     

运动车辆梁模型的横向振动频率及模态

将运动车辆的车身模型化为Eder-Bernoulli梁,车轮模型化为梁两端边界处的弹性不等的弹簧,形成半车模型.通过复模态分析法研究平滑路面上移动车体的横向振动特性,给出车体横向振动的频率方程以及模态的表达式,通过数值方法求解系统固有频率以及模态函数.并通过数值算例研究车辆运行速度、车体刚度、轮胎弹性系数对车体横向振动...     

随机风作用下高速列车动力学参数的可靠性优化设计

建立随机风作用下高速列车动力学参数的可靠性优化设计方法．首先考虑自然风的脉动特性，采用Cooper理论和谐波叠加法模拟随车移动点的脉动风速，给出随机风作用下高速列车非定常气动载荷的计算方法．然后建立高速列车车辆系统动力学模型，计算高速列车的运行安全性，并基于可靠性理论，给出随机风作用下高速列车失效概率的计算方法．在此基础上，以高速列车动力学参数为优化设计变量，以失效概率和轮轴横向力为优化目标，采用多目标遗传算法NSGA—II进行动力学参数的自动寻优，建立随机风作用下高速列车动力学参数的可靠性优化设计模型．经可靠性优化计算，高速列车的失效概率由原始的0．4884降低为0．1406，轮轴横向力由原始的45．13kN降低为43．01kN．通过优化高速列车动力学参数可以显著改善随机风作用下高速列车的运行安全性．     

车辆平顺性的虚拟现实仿真技术

探讨了基于虚拟现实的车辆平顺性振动仿真原理，利用软件编程的方法实现了汽车在虚拟道路上的行驶运动。汽车行驶时，能够表现车箱(包括驾驶员)的垂向运动、横向运动和俯仰运动，驾驶员的垂向运动、横向运动和俯仰运动，汽车轮胎的旋转运动。其运动规律皆由车辆平顺性动力学分析数据来确定。从而实现了车辆平顺性振动运动在计算机屏幕上的虚拟显示，以图形图像运动来直观的表现车辆的平顺性。     

可变速智能车辆车道变换控制研究

提出了一种能够兼顾横向控制和纵向控制的可变速自动换道控制方法。首先建立了可变速的车辆运动方程,并采用基于动态目标位置概念的控制机制,以模糊逻辑为控制策略,以T-S模糊模型为控制结构,以自适应神经网络为隶属度函数的参数调整手段,设计出一种兼顾智能车辆横向控制和纵向控制的运动控制器。仿真结果表明,提出的控制方法是可行的、有效的,并且较为理想地模拟实际交通环境中车辆换道的行为特性。     

加速过程偏角不对中轴系横向振动特性研究

建立了非稳态油膜力作用下，偏角不对中碰摩轴系的动力学模型，通过引入轴系问的位移约束，由Lagrange方程导出了不对中轴系的振动方程，研究了加速条件下，轴系横向振动特性随加速度、偏角不对中量的变化规律．研究表明：偏角不对中的存在，增强了系统的非线性和运动耦合；偏角不对中、轴承油膜、局部碰摩等因素一起，通过自激励、参数激励和外激励等方式激起了轴系的非线性振动响应；加速工况下，偏角不对中轴系横向振动出现了新的共振特性．     

车辆动力学与控制研究进展

从车辆纵、横、垂向动力学三个方面,结合电动汽车、智能网联等热点问题,介绍了目前车辆动力学与控制领域的研究成果.车辆纵向动力学方面包括车辆传动系统换挡控制、制动系统的设计与控制以及车辆状态的参数估计;车辆横向动力学方面涉及车辆转向系统设计与横向稳定性控制;车辆垂向动力学方面包括悬架系统的优化设计与半/主动控制.专刊研究成...     

人机共驾智能车辆车道偏离横向辅助控制仿真

车道偏离辅助控制对于提高人机共驾智能车辆行驶安全性具有重要的现实意义,为此进行人机共驾智能车辆车道偏离横向辅助控制仿真研究.对车道线进行识别与跟踪,并采用基于车辆当前位置和基于车辆跨越车道线时间的联合算法进行智能车辆车道偏离判断;建立车辆系统模型和驾驶员模型,并通过基于转向和制动的可拓联合方法实现车辆偏离的横向辅助控制.实验结果表明,所研究方法在转向反应时间、车道偏离持续时间、最大转向角速度与最大横向偏离量四个指标的表现上更好,人机共驾智能车辆车道偏离横向辅助控制效果更佳.     

基于RBF神经网络滑模控制的车辆横向控制研究

针对车辆横向控制系统中滑模控制器存在的抖振现象对转向机械结构带来的损耗问题,提出了一种基于RBF神经网络的滑模控制算法。利用RBF神经网络较强的自学习能力实时在线调节滑模控制器的切换项增益参数,增强系统的抗干扰能力与动态性能。将车辆实际参数代入仿真数学模型中,在Simulink仿真环境中进行对比仿真实验,仿真结果表明:该控制算法跟踪性能好,能够有效降低滑模控制器的抖振,满足车辆横向控制要求。     

Lateral motion control for four-wheel-independent-drive electric vehicles using optimal torque allocation and dynamic message priority scheduling

In this paper, the vehicle lateral motion control of four-wheel-independent-drive electric vehicles (4WID-EVs) with combined active front steering (AFS) and direct yaw moment control (DYC) through in-vehicle networks is studied. As a typical over-actuated system, a 4WID-EV requires a control allocation algorithm to achieve the generalized control efforts. In this paper, a quadratic programming (QP) based torque allocation algorithm is proposed with the advantage of equally and reasonably utilizing the tire-road friction of each wheel. It is also well known that the in-vehicle network and x-by-wire technologies have considerable advantages over the traditional point-to-point communications, and bring great strengths to complex control systems such as 4WID-EVs. However, there are also bandwidth limitations which would lead to message time-delays in in-vehicle network communications and degradation of control performance. The paper also proposes a mechanism to effectively utilize the limited network bandwidth resources and attenuate the adverse impact of in-vehicle network-induced time-delays, based on the idea of dynamic message priority scheduling. Simulation results from a high-fidelity vehicle model show that the proposed control architecture with the torque allocation algorithm and message dynamic-priority scheduling procedure can effectively improve the vehicle lateral motion control performance, and significantly reduce the adverse impact of the in-vehicle network message time-delays in the simulated maneuvers.     

基于横纵向联合控制的多目标优化车辆跟驰研究

为解决车辆在拥堵环境中因车速波动较大所带来的跟驰平稳性较差、跟踪无效或不安全等问题,提出了基于车辆模型和深度强化学习的多目标优化跟驰方案。首先基于车辆横纵向动力学建立车辆跟驰模型,然后根据车间距误差、速度误差、横向偏差及相对偏航角等,利用深度确定性策略梯度算法得到跟驰车的加速度和转向角,以更平稳安全地控制跟驰车辆。经NGSIM公开驾驶数据集进行测试与验证,该方案可有效地提升跟驰车辆的稳定、舒适与安全性,对保证交通安全和提升道路通行能力具有重要意义。     

基于车体横移振动的高速列车虚拟复合阻尼天棚控制算法研究

高速列车车体横向随机振动由车体的横移振动、侧滚振动和摇头振动三自由度合成,是影响车体横向运行平稳性的关键;为了改善列车横向运行平稳性,提高半主动控制性能;通过建立某型高速列车动力学模型,对车体横向振动特性进行分析,得出横移振动的加剧是造成车体合成横向振动和横向平稳性恶化的主要原因;通过分析在传统天棚阻尼控制算法下分别以车体合成横向振动为反馈和以横移振动为反馈对车体横向振动的控制效果,得出采用以车体横移振动为反馈的传统天棚阻尼控制算法对车体横向振动的抑制效果更佳;在此基础上,提出一种以车体横移振动为反馈的虚拟复合阻尼天棚控制算法,并进行联合仿真分析;结果表明:相比于被动控制,采用虚拟复合阻尼天棚控制算法后,车体合成横向振动加速度峰值、均方根值和平稳性改善率分别达到了46%、43%和19.5%,均高于采用传统天棚阻尼控制算法;可见,采用虚拟复合阻尼天棚控制算法在抑制车体合成横向振动,改善车体横向平稳性方面控制性能更佳。     

高速列车多目标约束横向半主动控制算法研究

传统的高速列车横向半主动控制研究,主要以提高车体横向运行平稳性为目的;但车体与各部件之间通过二系悬挂与一系悬挂连接传递相互耦合振动作用,因此半主动控制在改善车体横向平稳性的同时,将导致构架横向振动和轮轨横向作用加剧,从而使得列车脱轨系数增大,列车运行安全性能变差;针对这一问题,提出在虚拟惯性阻尼半主动控制和脱轨安全半主动控制的基础上,设计两个控制回路的多目标约束下的混合半主动控制算法,在实现列车横向半主动控制提高平稳性的同时,控制脱轨系数的恶化,从而同时提高列车平稳性和脱轨安全性能;并利用Simpack建立了某型高速列车多刚体动力学模型,联合Matlab/Simulink建立了联合仿真分析系统对车体横向半主动控制进行研究;结果表明:采用多目标约束半主动控制算法后,车体横向振动加速度峰值和均方根值分别降低了36%和34%,平稳性改善了15%,脱轨系数减小了17%;可见采用多目标约束半主动控制算法不仅能够有效抑制车体横向振动,改善列车运行平稳性,而且还能减小列车脱轨系数,提高列车运行安全性。     

基于模糊PID控制的列车主动悬架振动控制研究

为了研究高速列车主动悬架系统横向振动控制,将研究对象简化为二自由度1/4车悬挂模型,研究了高速列车车体的横移、侧滚为变量的列车悬挂控制系统,采用德国轨道谱作为列车的激励拟合。算法控制器选择方面,在PID控制和模糊控制的基础上,建立模糊PID控制,并建立了基于模糊PID控制的高速列车主动悬架横向振动控制系统,通过MATLAB仿真生成关系曲线,验证了模糊PID控制算法具有更好的控制效果。     

A novel integrated control framework of AFS,ASS, and DYC based on ideal roll angle to improve vehicle stability

The current research on vehicle stability control mainly focuses on following the ideal yaw rate and sideslip angle, without considering the potential of ideal roll angle in improving the vehicle stability. In addition, the mutation of tire-road friction coefficient promotes a great challenge to the stability control. To improve the vehicle stability, in this study, firstly, the three-dimensional stability region of “lateral speed-yaw rate-roll angle” was studied, and a method to determine the ideal roll angle was proposed. Secondly, a novel integrated control framework of AFS, ASS, and DYC based on ideal roll angle was proposed to actively control the front tire slip angles, suspension forces, and motor torques: In the upper-level controller, model predictive control and tire force distribution algorithm were used to obtain the optimal four-tire longitudinal forces, front tire lateral forces and additional roll moment under constraints; In the lower-level controller, the upper virtual target was realized by the optimal allocation algorithm of actuators and the tire slip controller. Finally, the proposed control framework was verified on the varied-µ road. The results indicated that compared with the two existing control strategies, the proposed framework can significantly improve the vehicle following performance and stability.     

无人驾驶车辆稳态漂移的无模型自适应控制

本文引入一种基于无模型自适应控制算法的无人驾驶车辆稳态漂移控制方案.首先,对车辆漂移动态过程进行动力学分析,其次,确定控制系统的控制输入量和控制目标,然后基于无人驾驶车辆稳态漂移控制系统的动态线性化数据模型,设计无模型自适应稳态漂移控制算法、伪雅可比矩阵估计整定算法和伪雅可比矩阵重置算法,从而实现无人驾驶车辆的稳态漂移...     

A novel fused neural network controller for lateral control of autonomous vehicles

We present a novel fused feed-forward neural network controller inspired by the notion of task decomposition principle. The controller is structurally simple and can be applied to a class of control systems that their control requires manipulation of two input variables. The benchmark problem of inverted pendulum is such example that its control requires availability of the angle as well as the displacement. We demonstrate that the lateral control of autonomous vehicles belongs to this class of systems and successfully apply the proposed controller to this problem. The parameters of the controller are encoded into real value chromosomes for genetic algorithm (GA) optimization. The neural network controller contains three neurons and six connection weights implying a small search space implying faster optimization time due to few controller parameters. The controller is also tested on two benchmark control problems of inverted pendulum and the ball-and-beam system. In particular, we apply the controller to lateral control of a prototype semi-autonomous vehicle. Simulation results suggest a good performance for all the tested systems. To demonstrate the robustness of the controller, we conduct Monte-Carlo evaluations when the system is subjected to random parameter uncertainty. Finally experimental studies on the lateral control of a prototype autonomous vehicle with different speed of operation are included. The simulation and experimental studies suggest the feasibility of this controller for numerous applications.     

Nonlinear cascade strategy for longitudinal control in automated vehicle guidance

This paper deals with automatic control design for automotive driving with a special focus on the longitudinal control. The automotive vehicle is a complex system characterised by highly nonlinear longitudinal and lateral coupled dynamics. Consequently, the control design for automated driving should deal with both of these dynamic couplings. Indeed, the longitudinal control plays an important role in the automated guidance to ensure safety and comfort of automotive passengers. In this work, a nonlinear cascade longitudinal control based on inner and outer-loops design is proposed. The lateral control is handled following a model predictive approach ensuring the automated steering of the vehicle. Finally, the nonlinear longitudinal control is integrated with the lateral control in a whole architecture to perform a coupled longitudinal and lateral control. The effectiveness of the automated driving strategy is highlighted through simulation results.